Clustering using firefly algorithm: Performance study

A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, that simulates the flash pattern and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. In this paper, the FA is used for clustering on benchmark problems and the performance of the FA is compared with other two nature inspired techniques — Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used in the literature. Thirteen typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. From the results obtained, we compare the performance of the FA algorithm and conclude that the FA can be efficiently used for clustering.

► In this paper, we present the clustering using the Firefly Algorithm (FA). ► To demonstrate the FA, 13 benchmark data sets from the UCI repository are used. ► The performance of the FA is compared with the 11 methods used in the literature. ► From the results, we conclude that the FA is as good as 11 methods.